Rick Larrick recently told Decision Science News that baseball players have been getting better over the years in a couple ways.

First, home runs and strikeouts have increased. The careless or clueless reader might note that this is curious, for from the batter’s perspective home runs are a good thing and strikeouts are a bad thing. What’s going on? Batters may be swinging harder, increasing the chance of both. The purported improvement is a result of the benefit of a home run being greater than the cost of a strikeout. After all, a home run results in at least one run, often more, and runs are a big deal since the typical team earns only about 5 of them per game.

DSN wondered how the players learned to swing harder from one decade to the next. Was it based on feedback from coaches? Or from fans / media attention?

According to Larrick, the number of attempted stolen bases has decreased over the years. Apparently, it is only worth it to steal if one can pull a very high percentage of the time, higher than had been believed in previous years (anyone know the stat?). So while crowds (presumably) like the action of stolen bases, players do not respond by doing it more. Winning seems more important than pleasing the crowd, which is a strike against the fan-feedback hypothesis.

After our post on winning back-to-back baseball games, some folks like our friend Russ Smith made comparisons to the hot hand effect. There is something to it. However, in the baseball example one starts with a prior of .5 (since one doesn’t even know which two teams are playing), while in basketball the chance a pro will make a free throw is about .75 (since one can condition on the player being a pro). What is surprising is that in both cases, the past success tells you next to nothing.

So, the question to the readers is: Why do some athletic abilities improve as history marches on (e.g., running speeds, batting, base-stealing) and others do not (e.g., free throws)?

P.S. For the record, Decision Science News is not becoming a sports blog. It is just a phase the Web site is going through. That said, there has been interest in seeing this kind of result in other sports, so that analysis will be coming in future posts, in glorious, glorious R and ggplot2. (Don’t know R yet? Learn by watching: R Video Tutorial 1, R Video Tutorial 2)

Photo credit: http://www.flickr.com/photos/cakecrumb/4398699952/. A cupcake was chosen because Jeff gave us empirical evidence that people like cupcakes much more than a control food.

The steroid argument can be tested. Consider the metrics of steroid users or suspected steroids and or compare metrics of whole populations of players in different time periods pre and post steroid era.

I doubt it’s leading to a widespread ability improvement.

Worth testing sure.

I’m curious if simple things like height and size of players is a factor in free throws. The league is dominated by big guys now. Dunkers and clunkers.

Where we night find an indicator is looking at collegiate ft percentages, where the population of players is much larger…

In terms of calculating the base stealing percentage needed to make it worth stealing, I think that would require quite a sophisticated model, which would be a whole lot of fun to try to create. You could probably get a crude number by using simply the odds a player will score on certain types of hits when they are on the current base vs. the next base, however, in reality the percentage would be dependent on a lot of factors, including the following.
• Which base is the runner on?
• What is the percent chance of him scoring from the current base and next base with a single, double or triple from the next batter (100% with a HR, close to 100% with a triple)?
• What are the attributes of the next batter?
o Odds of hitting single, double, triple, HR, walk, strike out, etc.
• If the next player walks there is no benefit from the steal.
• How many outs are there?
o Avoiding a double play – giving more players the opportunity to drive in the run is another benefit of stealing, this is irrelevant with 2 outs.
o If the next player strikes out and there are less than 2 outs, what are the attributes of the batter after him?
• How do the attributes of each batter change based on what type of swing they will try to execute, i.e. swing for fences, try to get it in play, bunt?
• What type of swing should they try to execute?
• What are the attributes of the current opposing pitcher and how does it change the attributes of the batters?
• Theoretically, if a manager were to actually use a model simulating this the opposing manager would want to use the same model and execute counter strategies bringing game theory into play. For instance;
o A manager could call for a steal and then for the next batter to swing for the fences which would only make sense from a game theory perspective because it basically negates the benefits of the successful steal.
o Opposing managers could pitch out in steal situations lowering the chance of a successful steal.
o Pitching changes could be made, pitch hitters could be used.

The analysis could be further expanded to determine
• How all the factors change in a hit and run scenario.
• How to form your line up based on the attributes of all the players and based on the line up which players should be encouraged to steal and when.
• Which players to add in the offseason to maximize run output.

Olympic weightlifting is a sport with improvement over time. The poundages lifted in the snatch and the clean and jerk are now way beyond what they were in the 1960s, say. The rules changed in a few respects (e.g., the bar can touch the body on the way up, you can move your hands wider, changing your grip at the shoulders) but these don’t seem enough to account for the higher poundages.